Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models often ignore the underlying PV conversion process and may produce predictions that are outside the operational range of a PV system. In this paper, we propose a physics-informed state space framework that combines a differentiable PV conversion model with a selective state space backbone (PowerMamba). Our approach follows a physics-residual decomposition. The physics module provides a structured baseline based on solar geometry, irradiance, temperature effects, and inverter limits, while the state space model learns residual corrections from numerical weather prediction (NWP) inputs over a 24-hour horizon. Experiments on one year of utility-scale PV data show that the proposed method significantly improves forecasting accuracy, reducing RMSE by 38.5% and MAE by 45.8% compared to a physics-only baseline, and outperforming a purely data-driven model. Results further show that incorporating system-level physics provides the largest performance gains, while additional refinements offer only marginal improvements. Beyond accuracy, the model improves robustness by constraining predictions toward physical feasibility, reducing unrealistic predictions under corrupted measurements.
Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants
i-EM S.r.l（i-EM有限公司）；Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa（比萨大学能源、系统、领土和建筑工程系）；Enel Green Power S.p.A.（意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；Renewable Energy Management, Enel Green Power S.p.A.（可再生能源管理，意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；University of Pisa（比萨大学）
MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
Department of Naval, Electrical, Electronics；Telecommunications Engineering, University of Genoa, Via all’Opera Pia 11a, 16145 Genoa, Italy；Unit of Innovation, Entrepreneurship \& Sustainability, Department of Engineering, University Campus Bio-Medico of Rome Via Alvaro del Portillo 21, 00128 Rome, Italy；Computer Systems Department of Engineering, University Campus Bio-Medico of Rome Via Alvaro del Portillo 21, 00128 Rome, Italy
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校西贝尔计算与数据科学学院）；Department of Chemical & Biomolecular Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校化学与生物分子工程系）
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（工业与系统工程系，佐治亚理工学院）；AI Institute for Advances in Optimization（优化前沿人工智能研究所）
Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS
Politecnico di Bari（巴里理工大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；CNIT, Consorzio Nazionale Interuniversitario per le Telecomunicazioni（意大利国家跨大学电信联盟）；Exprivia S.p.A.（Exprivia公司）；Sitael S.p.A.（Sitael公司）；Fincons Group S.p.A.（Fincons集团）
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
Mondragon University（蒙dragon大学）；University of the Basque Country (UPV/EHU)（巴斯克大学）；Basque Centre for Applied Mathematics（巴斯克应用数学中心）；KTH, Royal Institute of Technology（皇家理工学院）；Ikerbasque, Basque Foundation for Science（巴斯克科学基金会）
Forecasting the future development in quality and value of professional football players
1 Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands；\ 2 Data Science \& Computer Vision, SciSports, 3703 HX Zeist, The Netherlands
